The white matter of the brain contains nerve fibers. Many of these nerve fibers
(axons) are surrounded by a type of fat called myelin. The myelin gives the
whitish appearance to the white matter. Myelin acts as an insulator and it increases
the speed of transmission of all nerve signals. The prevalence of white matter
hyperintensities increases with age. White matter hyperintensities are associated
with impaired cognition, balance and gait (Murray et
al., 2005). Cerebral white matter undergoes various degenerative changes
with normal aging, including decreases in myelin density and alterations in
myelin structure. The association between major depressive disorder and increased
prevalence of brain white-matter hyperintensities has been reported in elderly
people (Iosifescu et al., 2006). CWM lesions
are observed frequently in human ischemic cerebrovascular disease and have been
thought to contribute to cognitive impairment (Wakita et
The morphology of the brain manifests, for example, the so-called cerebral
white matter exhibits degenerative changes with normal aging. Such changes are
observed as decrease in myelin density as well as alterations in myelin structure
(Ota et al., 2006). Assessing the texture of
such degenerative CMW can be done via Diffusion Tensor Magnetic Resonance
Imaging (DTMRI) technique. It is a non-invasive tool for determining white matter
connectivity in the brain. This modality images by measuring the water molecule
motion within the tissues, using magnetic resonance techniques David
and Rachid, (2003) and hence could provide directional information of the
microtubular structure (Davoodi-Bojd and Soltanian-Zadeh,
2009). The self-diffusion of water molecules in the human brain is affected
by the underlying tissue structure. Water diffuses more along fiber tracts than
across the fiber tracts. A myelinated CWM tract can pose more restriction on
water molecules than an unmyelinated tract. So by monitoring diffusion of water
with DTMRI information on tissue structure can be obtained ziz (Ulug,
In this study, DTMR images that are obtained under normal and pathological
conditions are subjected to statistical method of texture analysis. Texture
features provide integral, quantitative information about structural properties
at a millimeter scale (Kovalev and Kruggel, 2007). This
texture pattern of CWM changes with respect to age and depending on neurological
conditions of the subject. Commensurate with the complex system profile of the
brain (and its white matter), described in this study is an Artificial Neural
Network (ANN)-based approach to relate age versus the extent of morphological
changes observed in the clinical scans (with DTMRI) of the brains of human subjects
(in the age group of about 50 to 80 years). Experimental/clinical data on the
brain complex vis-α-vis ageing profile of the subjects are validated using
a test ANN. This study could lead to understanding the morphology of CWM versus
age. ANN is used to solve a vast variety of problems in science and engineering;
particularly in some areas where the conventional modeling method may be too
complex or may not give good result. Inspired from biological neuron, a well
trained ANN would be able to become a predictive model for a specific application
(Rashid et al., 2010). Back propagation neural
network (BPNN) is one of the most popular models applied in various fields.
The theory and algorithm has been clearly defined by the propagation rule i.e.
generalized delta rule (Chiu et al., 2008).
The brain is a structure made of innumerable interconnected neural cellular
parts that interact across the spatial domain; also, the associated neural activity
depicts a temporal interaction between the cells. Further, depending on the
nature of temporal activity involved and spatial proliferation of the neural
information, the underlying interaction is largely stochastical and partly deterministic.
In this perspective, as in any complex system, a partial change in the physical
organization and/or functional activity would lead to a significant change in
the self-organizational attributes of the system as indicated by Wakita
et al. (2002). Pertinent to the brain complex, the normal ageing
process will cause distinct degenerative alterations in the mass of the brain,
normally observed as decreases in myelin density as well as alterations in myelin
structure across the CWM. Knowing such changes can lead to diagnosing neurological
disorders in elderly people. Hence attempts of this research are the following:
||Age versus degenerative attributes of the CWM: Assessment
via an ANN trained with clinical data
||Correlating the age to the extent of clinically observed morphology in
the CWM under neurological normal and abnormal conditions
MATERIALS AND METHODS
Work flow: DTMR images are obtained from 48 volunteers of age group
between 50 and 80 are segmented for white matter using automated segmenting
procedure and analyzed for age-related changes in brain fiber tracts. The procedure
adopted is as follows:
||Obtaining the DTMR images from the volunteers under normal
and abnormal conditions
||Segmenting the image for white matter
||Finding co-occurrence matrix of the quantised white matter image
||Finding the appropriate statistical features from GLCM
||Classifying pathological images
||Using back propagation neural network, the image is classified against
Image acquisition: MRI acquisition was performed on a GE 3 Tesla Signa HDX system equipped with a 8-channel brain array coil using a Diffusion Tensor Imaging with Fourier transform. Protocol: Field-of-view (FOV) 240x240 mm; matrix 256x256; TR = 7400; Number of diffusion direction = 25; b value = 1000; voxel size 0.9375x0.9375x5 mm; slice thickness = 5 mm; scanning time = 8 min. Scanning done in axial plane parallel to the long axis of the body of corpus callosum. Total of 48 images under age group of 50 to 80 years with no neurological disorders (with mean age = 64.4483, SD = 8.7841) from both male and female, with no neurological problem, another 5 images between the age 60 and 70 (mean = 65 , SD = 3.1113) with cerebral infarction and one 54 years old HIV+ case are considered for the purpose. Image analysis methods are implemented in MATLABTM (Version 7.6).
DTMR imaging is a noninvasive tool for determining white matter connectivity
in the brain by measuring random motion of water molecules which is called diffusion.
This motion is restricted in the axons due to the existence of myelin sheath
and hence the important characteristic of DTMR imaging is to fetch the directional
information of microtubule living structure (Davoodi-Bojd
and Soltanian-Zadeh, 2009). Axial view of DTMRI modality, images both gray
matter and white matter tissues, since anisotropy is dominant in the latter,
segmenting of this tissue alone becomes necessary.
ENVISAGING CLINICAL DATA ON ANN
The procedure adopted in this study to correlate changes in BWM with respect
to age involves acquisition of clinical DTMRI data, analysis of the data to
capture its statistical features and application of the collected data on an
ANN towards the intended prediction of age versus observed changes in
||Flow-chart depicting ANN-based test procedure to correlate
the changes measured in BWM versus aging. GLCM: Gray level co-occurrence
matrix; BPNN: Backpropagation neural network (used as the test ANN)
These steps are indicated in Fig. 1 as a flow chart:
An image is not just a random collection of pixels; it is a meaningful arrangement of regions and objects. A region in an image has a constant texture if a set of local statistics or other local properties of the picture function are constant, slowly varying, or approximately periodic. Image texture, defined as a function of the spatial variation in pixel intensities (gray values), is useful in a variety of applications and has been a subject of intense study by many researchers. One immediate application of image texture is the recognition of image regions using texture properties. The analysis of texture parameters is a useful way of increasing the information obtainable from medical images with applications ranging from the segmentation of specific anatomical structures to detection of a lesion. Texture analysis can be approached by any of the methods like geometrical, statistical, model-based and signal processing methods.
Depending on the complexity of the image and amount of information to be extracted
from the image, statistical method is divided into first order, second order
and higher order statistics. First order statistics estimates mean, variance,
skewness and flatness. Method based on second-order statistics uses GLCM to
extract features. Statistical methods analyze the spatial distribution of gray
values, by computing local features at each point in the image and deriving
a set of statistics from the distributions of the local features. The reason
behind this is the fact that the spatial distribution of gray values is one
of the defining qualities of texture (Srinivasan and Shobha,
Gray level co-occurrence matrix (GLCM): Images containing repeating
patterns are said to be textured. Texture analysis of such images
mainly concerns with feature extraction and image coding. An application of
image texture analysis, for example, is the recognition of image regions in
terms of texture properties. By representing a complex texture with a small
number of measurable features or parameters, significant dimension reduction
is feasible enabling automated texture processing. Texture analysis implies
formulating a set of statistical measures on the image section being analyzed.
The texture is an important characteristic in the analysis of many types of
images. Texture classification consists in partitioning a set of images into
different classes in such a way that all images belonging to the same class
are homogeneously textured. The fundamental problem in texture classification
is to determine a proper set of features that can be used to make a distinction
between textures (Loum et al., 2007).
The GLCM estimates image properties in terms of second-order statistics defined via co-occurrence matrix of the gray level (GLCM). This second order statistics corresponds to the likelihood of observing a pair of voxel v1 and v2 separated by a distance vector dxy in 2D space (x, y). That is, considering a pair of voxel with intensities i and j occurring at some distance dxy apart, the co-occurrence matrix measures the frequency that a gray scale value appears in relation to another grayscale value on the image. Explicitly, the co-occurrence is defined by:
where, I is the sub-area under consideration and (Δx, Δy) is the
distance vector between corresponding voxels. Further, f(x, y) is the function
that describes the 2D map of the image. In the present work, average of four
co-occurrence matrices in the direction of 0, 45, 90 and 135 degrees is taken
to ensure rotational invariance as suggested by Caban et
Haralick et al. (1973) has proposed fourteen
textural features that can be derived from GLCM for the purpose of classifying
images on the basis of texture properties (Freeborough Fox,
1998). Given an image composed of pixels each with intensity (a specific
gray level), the GLCM indicated earlier is a tabulation of how often different
combinations of gray levels co-occur in an image or an image section. It estimates
image properties related to second-order statistics. These image properties
(commonly known as Haralick texture features) can be used for image classification
(Haralick et al., 1973). Explicitly, the fourteen
features extracted from an image exhaustively represents the characteristics
of the image and they depict, (1) correlation; (2) contrast (3) variance; (4)
inverse difference moment; (5) angular second moment; (6) sum average; (7) sum
variance; (8) mean; (9) standard deviation; (10) sum entropy; (11) relative
entropy; (12) difference variance; (13) information measure of correlation and
(14) difference entropy.
Thus, the qualitative aspects of the image textural features can be translated into their corresponding quantitative feature data set in terms of the associated statistical features using GLCM described above. In addition, first-order statistics indicated below can also be considered to quantify textural features. It is approximate but simpler format of feature representation.
This first order statistical texture analysis computes local features at each
point in a texture image and derives a set of statistics from the local features
defined by the combination of intensities at specified positions relative to
each point in the image. These (first order) texture features denote mean, standard
deviation, Shannon entropy and variance (Sharma et al.,
The acquired images are segmented for white matter GLCM is computed at 0, 45, 90 and 135 degrees for all the samples. For each GLCM four Haralick parameters like inverse difference moment, angular second moment, entropy and information measure of correlation are computed. These results in 4*4 = 16 measures for each sample. To ensure the rotational invariance mean value over each feature that was obtained from four directions is computed, totaling four measures for each sample. These are the feature values obtained through second order statistics. First order statistical features like mean, standard deviation, entropy and variance are directly computed for the acquired images.
CONSTRUCTION OF NEURAL NETWORK
Artificial Neural Netwrok (ANN) can provide suitable solutions for highly complex
and non-linear problems in classifying the inputs. Back Propagation Neural Network
(BPNN) algorithm is suitable for pattern recognition and it is based on weight
error correction rules.
|| Architecture of the constructed BPNN
The network constructed (Fig. 2) for this study consists
of two hidden layers with eight neurons in each layer, a single output and four
input nodes. Four different networks are used to classify normal and abnormal
CWMs. Network weights are updated according to resilient backpropagation
where xi, yj, b, zk and O are the input, output of first layer, bias, output of second layer and final output respectively for i = n = 1 to 4, j = k = m = 1 to 8.
ANN training: The ANN simulation has two phases. First, a training input pattern is presented to the network-input layer. The network propagates the input pattern from layer-to-layer until the output neuron generates an output pattern. If this pattern is different from the desired (teacher) output, an error is calculated and then propagated backward through the network from the output layer to the input layer. The weights of the interconnections are then modified proportional to the gradient of the error. (Initial settings of the weights are taken as random numbers (0 to 1) of uniform distribution).
Corresponding to GLCM approach, the textural feature map of the image corresponds to a matrix of fourteen parameters. But, only a subset of this matrix containing four features is given as input to the network.
The network is trained to recognize the various data sets obtained from different samples supervised by the preset teacher value on age at the output. Depending on the error (depicting the difference between output and teacher value), network weights are updated according to resilient backpropagation algorithm.
Traditional sigmoidal transfer function of hyperbolic tangent is used between first hidden layer and second hidden layer. This transfer function enables squashing of the output towards convergence. (Sigmoidal and linear functions are used in and between second hidden layer and output layer. The linear function sets a gin/scaling on the output).
In the simulations carried out, the network reads four feature values (out of fourteen) from each of forty eight clinical volunteers and forms a matrix of size 4x48 with corresponding supervisory vector of 48x1. Learning is accomplished by successively adjusting the weights based on a set of input patterns and the corresponding set of desired output values on age. The above said learning process is also carried out by taking four first order features as input to the constructed ANN.
The learning process continues until the network responds with output signals such that the sum of root mean square errors from the output signals is less than preset or stop criteria. The network is now trained to recognize the age available as output vector corresponding to the four input features. The stop criteria indicated above allow the training to stop whenever a maximum number of (pre-specified) epochs occur or when the performance goal of error being 0.001 is met.
To evaluate the age-related changes in brain fiber tracts, the texture anisotropy of the BWM is examined using textural features obtained from real-time DTMR images. As mentioned earlier, these images are obtained from adults with no brain-related pathology under the age group between 50 and 80 years at a particular location of the brain parallel to the long axis of the body of Corpus callosum. Textural features are computed for each sample and stored in data base.
Analysis on normal condition: To evaluate the age related changes in
brain fiber tracts, texture anisotropy is examined using textural features obtained
from real-time DTMR images. Figure 3a and b
shows a slice of DTMRI images under normal and abnormal conditions.
BPNN is trained with four features as input and apriori known age as output.
The network is trained separately with first order features and with second
||DTMRI 2D-brain slice: (a) image from normal brain and (b)
image obtained from stroke affected l brain (cerebral infarction))
The network converges for given input and output. Training curve that is obtained
is shown in Fig. 4. Now the trained ANN is tested using five
unknown sample (data that was not used in training). Also, the predicted age
in each case is compared with the age known a priori. The percentage of accuracy
of the network lies in the range 90 to 96.
Analysis on infarct condition: A sample of brain white matter with cerebral
infarction is shown in Fig. 3b. Used in this study are five
subjects in the age group of 60 to 70 years and are diagnosed as stroke (cerebral
infarction). These images are processed for textural features and classified
using the trained BPNN.
||BPNN training curve (a) first order parameters as input to
BPNN (b) second order parameters as input to BPNN
|| Segmented BWM images, (a and b) normal brain, (c) stroke
affected brain and (d) HIV infected brain
They are classified as approximately 10 to 12 years older than their original
age leading to network accuracy approximately as 80%.
Analysis on HIV+ case: One male subject infected by HIV+ was presented to the clinic with difficulty in moving his limbs. He was 54 years old and was diagnosed as Progressive Multifocal Leukoencephalopathy (PML). His BWM was damaged and worst demyelination was observed. When this clinical DTMRI was processed using the proposed technique, the constructed BPNN could not recognize age of the subject. Segmented images under normal, infarct and HIV+ cases are shown in Fig. 5.
The brain consists of more than 100 billion neurons and each of these neurons
has one axon. Each axon branch communicates with multiple neurons. Notably,
axons that share similar destinations tend to form bundles and as they travel
toward the deep regions of the brain to achieve long-range connections, they
form huge axonal bundles called white matter tracts (Faria
et al., 2010). Courchesne et al. (2000)
claim that morphology of brain changes throughout the life. The value increases
from child hood to 4th decade of life and starts decreasing from late 5th decade
onwards and also almost static condition was observed in-between. However the
present study is between 50 and 80 years, where the obtained features from the
image sample changes such that they were able to be modeled using BPNN and hence
age was predicted correctly under normal conditions. However, when the constructed
BPNN is used for subjects with neurological problems, the network could not
recognize correctly because of change in feature value. This is due to the fact
that the computed features replicates the morphology of BWM and such type of
study is also discussed by Kalpana et al. (2011).
Christos and Susan (2002), claims that their results
provide the first demonstration of longitudinal age changes in gray matter and
white matter tissue contrast and in regional WM signal intensities, the latter
implying degenerative age changes in WM connectivity. Factors that may lead
to signal changes include WM demyelination and changes in water, protein and
mineral content of tissue. They also claim the possibility of neuropathological
abnormalities, such as deposition of amyloidal plaques, may be reflected in
signal changes in brain tissue.
Similar type of study is also performed by Freeborough
and Fox (1998), where they have computed GLCM, Haralick parameters and classified
using a discriminate function for control and Alzheimers disease. This
study correlates with the present proposed study in the way that here BPNN is
used for classification and pathological conditions taken are infarct and HIV+
instead of Alzheimers disease.
Alterations of the architecture of cerebral white matter in the developing human brain can affect cortical development and result in functional disabilities. In this study, brain maturation as reflected by changes in white matter density with age is investigated. Though the presence of white matter is found in many areas of the brain structure, CWM images are obtained from region parallel to long axis of corpus callosum. This is done because much fiber tracts are found in corpus callosum which interconnects two hemispheres of brain and also CWM is the focus of discussion in this study.
The following conclusions are drawn from this study:
||Fiber tracts of the brain changes with respect to age and
also based on neurological problems
||Adopted in this paper is the first order and second order statistical
technique. Four Haralicks feature parameters are extracted from GLCM
and four first order statistical features are extracted from the samples
||BPNN (with four features as input) is constructed to classify BWM based
on age under normal and abnormal conditions
||Though Haralick features are fourteen in number, any four features (a
subset of fourteen features) gone almost same result, which is discussed
in detail in Kalpana et al. (2010)
||Constructed BPNN could correctly recognize test images of controlled subjects.
It could recognize infracted subjects at different age of 10 to 12 years
more than the clinical value
||On the other hand the network could not recognize HIV+ case
||This could be due to the fact that high demyelination was clinically reported
in the present HIV+ case
||In stroke affected subjects BWM damage is comparatively much less
||Since the constructed BPNN is trained with textural features (that are
representatives of BWM morphology), any change in its value could alter
the result of the network (age)
||Since the level of demyelination in infarcted subject is much less than
HIV+ case, network could recognise stroke affected subjects at lesser accuracy,
whereas HIV+ case could not be recognized by the network
||This could also be observed in the figure 5, which shows
DTMRI, segmented BWM images on normal, infarct and HIV+ conditions